[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
针对现有位置预测研究多数忽略时间和空间之间关联性的问题,提出一种基于时空特性的长短期记忆模型(ST-LSTM)。基于LSTM网络添加单独处理用户移动行为时空信息的时空门,并考虑用户签到的时间及空间因素,从而使模型具有时空特性。在ST-L...针对现有位置预测研究多数忽略时间和空间之间关联性的问题,提出一种基于时空特性的长短期记忆模型(ST-LSTM)。基于LSTM网络添加单独处理用户移动行为时空信息的时空门,并考虑用户签到的时间及空间因素,从而使模型具有时空特性。在ST-LSTM网络中引入个人修正因子,对每类用户的输出结果进行修正,在确保基本特性的基础上突出个性化,更好地学习每类用户的行为轨迹特征,同时在保证ST-LSTM网络特性的前提下给出 2种 ST-LSTM网络的简化变体模型。在公开数据集上的测试结果表明,与主流位置预测方法相比,该预测模型精确率、召回率、 F 1值都有明显提升。展开更多
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
文摘针对现有位置预测研究多数忽略时间和空间之间关联性的问题,提出一种基于时空特性的长短期记忆模型(ST-LSTM)。基于LSTM网络添加单独处理用户移动行为时空信息的时空门,并考虑用户签到的时间及空间因素,从而使模型具有时空特性。在ST-LSTM网络中引入个人修正因子,对每类用户的输出结果进行修正,在确保基本特性的基础上突出个性化,更好地学习每类用户的行为轨迹特征,同时在保证ST-LSTM网络特性的前提下给出 2种 ST-LSTM网络的简化变体模型。在公开数据集上的测试结果表明,与主流位置预测方法相比,该预测模型精确率、召回率、 F 1值都有明显提升。
